IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4733-d1172019.html
   My bibliography  Save this article

Online Prediction of Electric Vehicle Battery Failure Using LSTM Network

Author

Listed:
  • Xuemei Li

    (School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Hao Chang

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
    School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Ruichao Wei

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China)

  • Shenshi Huang

    (School of Architectural Engineering, Shenzhen Polytechnic, Shenzhen 518055, China)

  • Shaozhang Chen

    (School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Zhiwei He

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
    School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Dongxu Ouyang

    (College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China)

Abstract

The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use.

Suggested Citation

  • Xuemei Li & Hao Chang & Ruichao Wei & Shenshi Huang & Shaozhang Chen & Zhiwei He & Dongxu Ouyang, 2023. "Online Prediction of Electric Vehicle Battery Failure Using LSTM Network," Energies, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4733-:d:1172019
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4733/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4733/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huiping Jiang & Rui Jiao & Zequn Wang & Ting Zhang & Licheng Wu & Ning Cai, 2021. "Construction and Analysis of Emotion Computing Model Based on LSTM," Complexity, Hindawi, vol. 2021, pages 1-12, February.
    2. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Prashant Kumar & Prince & Ashish Kumar Sinha & Heung Soo Kim, 2024. "Electric Vehicle Motor Fault Detection with Improved Recurrent 1D Convolutional Neural Network," Mathematics, MDPI, vol. 12(19), pages 1-17, September.
    2. Sercan Yalçın & Münür Sacit Herdem, 2024. "Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging," Energies, MDPI, vol. 17(12), pages 1-21, June.
    3. Tao Yan & Jizhong Chen & Dong Hui & Xiangjun Li & Delong Zhang, 2024. "The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    4. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    2. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    3. Zhijie Duan & Luo Zhang & Lili Feng & Shuguang Yu & Zengyou Jiang & Xiaoming Xu & Jichao Hong, 2021. "Research on Economic and Operating Characteristics of Hydrogen Fuel Cell Cars Based on Real Vehicle Tests," Energies, MDPI, vol. 14(23), pages 1-19, November.
    4. Daniels, Rojo Kurian & Kumar, Vikas & Chouhan, Satyendra Singh & Prabhakar, Aneesh, 2024. "Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization," Applied Energy, Elsevier, vol. 355(C).
    5. Qi, Kaijian & Zhang, Weigang & Zhou, Wei & Cheng, Jifu, 2022. "Integrated battery power capability prediction and driving torque regulation for electric vehicles: A reduced order MPC approach," Applied Energy, Elsevier, vol. 317(C).
    6. Li, Jinwen & Che, Yunhong & Zhang, Kai & Liu, Hongao & Zhuang, Yi & Liu, Congzhi & Hu, Xiaosong, 2024. "Efficient battery fault monitoring in electric vehicles: Advancing from detection to quantification," Energy, Elsevier, vol. 313(C).
    7. Mostofi Fatemeh & Toğan Vedat & Başağa Hasan Basri, 2022. "Real-estate price prediction with deep neural network and principal component analysis," Organization, Technology and Management in Construction, Sciendo, vol. 14(1), pages 2741-2759, January.
    8. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    9. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
    10. Zewen Meng & Tiezhu Zhang & Hongxin Zhang & Qinghai Zhao & Jian Yang, 2021. "Energy Management Strategy for an Electromechanical-Hydraulic Coupled Power Electric Vehicle Considering the Optimal Speed Threshold," Energies, MDPI, vol. 14(17), pages 1-12, August.
    11. Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
    12. Shen, Yudong & Wang, Xueyuan & Jiang, Zhao & Luo, Bingyin & Chen, Daidai & Wei, Xuezhe & Dai, Haifeng, 2024. "Online detection of lithium plating onset during constant and multistage constant current fast charging for lithium-ion batteries," Applied Energy, Elsevier, vol. 370(C).
    13. Hong, Jichao & Liang, Fengwei & Chen, Yingjie & Wang, Facheng & Zhang, Xinyang & Li, Kerui & Zhang, Huaqin & Yang, Jingsong & Zhang, Chi & Yang, Haixu & Ma, Shikun & Yang, Qianqian, 2024. "A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles," Energy, Elsevier, vol. 299(C).
    14. Jichao Hong & Fengwei Liang & Xun Gong & Xiaoming Xu & Quanqing Yu, 2022. "Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network," Energies, MDPI, vol. 15(24), pages 1-14, December.
    15. Chen, Dongfang & Pei, Pucheng & Ren, Peng & Song, Xin & Wang, He & Zhang, Lu & Wang, Mingkai, 2022. "Analytical methods for the effect of anode nitrogen concentration on performance and voltage consistency of proton exchange membrane fuel cell stack," Energy, Elsevier, vol. 258(C).
    16. Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).
    17. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Daniels, Rojo Kurian & Kumar, Vikas & Prabhakar, Aneesh, 2025. "A comparative study of data-driven thermal fault prediction using machine learning algorithms in air-cooled cylindrical Li-ion battery modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    19. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
    20. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4733-:d:1172019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.